Abstract Due to the exponential overflow of textual information in various fields knowledge and on internet, it is very challenging extract important or generate a summary from some multi-document collection specific field. With such gigantic amount content, human text summarization becomes impractical since expensive consumes lot time effort. So, developing automatic (ATS) systems becoming inc...

Pareto optimization solves a constrained optimization task by reformulating the task as a bi-objective problem. Pareto optimization has been shown quite effective in applications; however, it has little theoretical support. This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimiza...

Many real-world problems have a multiobjective character. A-posteriori techniques such as multiobjective evolutionary algorithms (MOEA) generate best compromise solution sets, i.e. Pareto-fronts and Pareto-sets. Classic MOEA are able to find quite efficiently good approximations of the complete Pareto-fronts also for very complex problems. In real-world applications only small sections of the c...

Multidisciplinary Design Optimization (MDO) problems can have a unique objective or be multi-objective. In this paper, we are interested in MDO problems having at least two conflicting objectives. This characteristic ensures the existence of a set of compromise solutions called Pareto front. We treat those MDO problems like Multi-Objective Optimization (MOO) problems. Actual MOO methods suffer ...

abstract in this paper, a fuzzy pid with new structure is proposed to solve the load frequency control in interconnected power systems. in this study, a new structure and effective of the fuzzy pid-type load frequency control (lfc) is proposed to solve the load frequency control in interconnected power systems. the main objective is to eliminate the deviations in the frequency of different area...

We address the problem of multi-objective constraint optimization problems (MO-COPs). Solving an MO-COP traditionally consists in computing the set of all Pareto solutions (i.e. the Pareto front). But this Pareto front is exponentially large in the general case. So this causes two main problems. First is the time complexity concerns. Second is a lack of decisiveness. In this paper, we present t...

To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms part...

In real world problems, one is often faced with the problem of multiple, possibly competing, goals, which should be optimized simultaneously. These competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. If none of the objectives have preference over the other, none of these trade-off solutions can be said to be better than any other solution in the set....